diff --git a/pyworkflow/em/packages/pkpd/bibtex.py b/pyworkflow/em/packages/pkpd/bibtex.py index 667fd54395..b32286f63d 100644 --- a/pyworkflow/em/packages/pkpd/bibtex.py +++ b/pyworkflow/em/packages/pkpd/bibtex.py @@ -70,6 +70,17 @@ Url = {http://link.springer.com/article/10.1023/A:1007572803027} } +@Article{Mahmood1996, + Title = {Quantitative prediction of in vivo drug-drug interactions from in vitro data based on physiological pharmacokinetics: use of maximum unbound concentration of inhibitor at the inlet to the liver}, + Author = {Mahmood, I. and Balian, J. D.}, + Journal = {J. Pharmaceutical sciences}, + Pages = {411-414}, + Volume = {85}, + Year = {1996}, + Doi = {http://dx.doi.org/10.1021/js950400y}, + Url = {http://onlinelibrary.wiley.com/doi/10.1021/js950400y/full} +} + @Article{Rostami2004, Title = {In silico simulations to assess the in vivo consequences of in vitro metabolic drug?drug interactions}, Author = {Rostami-Hodjegan, A. and Tucker, G. T.}, @@ -81,6 +92,17 @@ Url = {http://www.sciencedirect.com/science/article/pii/S174067490400037X} } +@Article{Sharma2009, + Title = {To scale or not to scale: the principles of dose extrapolation.}, + Author = {Sharma, V. and McNeill, J. H.}, + Journal = {British J. Pharmacology}, + Year = {2009}, + Pages = {907-921}, + Volume = {157}, + Doi = {http://dx.doi.org/10.1111/j.1476-5381.2009.00267.x}, + Url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2737649/pdf/bph0157-0907.pdf} +} + @Article{Spiess2010, Title = {An evaluation of R2 as an inadequate measure for nonlinear models in pharmacological and biochemical research: a Monte Carlo approach.}, Author = {Spiess, A. and Neumeyer, N.}, diff --git a/pyworkflow/em/packages/pkpd/protocol_pkpd_apply_allometric_scaling.py b/pyworkflow/em/packages/pkpd/protocol_pkpd_apply_allometric_scaling.py index 4feb6f86d3..4517e21be6 100644 --- a/pyworkflow/em/packages/pkpd/protocol_pkpd_apply_allometric_scaling.py +++ b/pyworkflow/em/packages/pkpd/protocol_pkpd_apply_allometric_scaling.py @@ -37,7 +37,9 @@ class ProtPKPDApplyAllometricScaling(ProtPKPD): """ Apply an allometric scaling previously calculated to an incoming experiment. The labels specified by the - allometric scaling model will be rescaled to the target weight. """ + allometric scaling model will be rescaled to the target weight. Note that depending on the exponent of the + fitting you may want to use a different predictor (weight*maximum lifespan potential, or weight*brain weight) + see the rule of exponents (Mahmood and Balian 1996). """ _label = 'apply allometric' @@ -103,3 +105,6 @@ def createOutputStep(self): def _summary(self): msg = ["Target weight: %f"%self.targetWeight.get()] return msg + + def _citations(self): + return ['Sharma2009','Mahmood1996'] \ No newline at end of file diff --git a/todo_list b/todo_list index dfaab4ceb9..c60a6f109c 100644 --- a/todo_list +++ b/todo_list @@ -1,3 +1,7 @@ +Fitting plot should be more continuous +Implement a test for allometric scaling and bootstrapping +A bootstrapped population could be changed the experiment it refers to + Generic analysis (signalAnalysis.py) Generic PD analysis Generic fitters (expression or func.py) @@ -10,10 +14,3 @@ Population comparison between simulated data Batch effect via PCA Bootstrap on PD models Variance Inflation Factor - -# Files to revise -wizard.py: createDrugSource, configureSource -tk_ode.py: configureSource -protocol_pkpd_ode_refine.py: configureSource -protocol_pkpd_ode_bootstrap.py: configureSource -protocol_pkpd_bootstrap_simulate.py: configureSource